Fixed and Random Effects Models
$9.99
The objective of this series of tutorials is to make the theory and application of Fixed Effects LSDV, Fixed Effects Within Model and Random Effects Model easier to understand. The tutorials discuss the important concepts related to Fixed and Random Effects models in detail using suitable examples and quizzes. For complete details on the learning objectives, go to the link here: Fixed and Random Effects Models.
Description
The objective of this series of tutorials is to make the theory and application of Fixed Effects LSDV, Fixed Effects Within Model and Random Effects Model easier to understand. The tutorials discuss the important concepts related to Fixed and Random Effects models in detail using suitable examples and quizzes. The tutorials cover the following topics in detail:
- Why do we need Panel Data Models?
- Heterogeneity in Panel Data
- Observed and Unobserved Heterogeneity
- Individual-specific effects, Time-specific effects and Two-way models
- Fixed Effects LSDV Approach
- How does the LSDV Model control for unobserved heterogeneity?
- Application of LSDV Model in R and Stata
- Fixed Effects: LSDV vs Within Model
- Fixed Effects “Within” Estimator
- How does the “Within” Model eliminate unobserved heterogeneity
- Derivation of the Fixed Effects “Within” Model equation
- Application of “Within” Model in R and Stata
- Estimating the individual-specific, time-specific and Two-way Fixed Effects in R and Stata
- Interpretation of Fixed Effects Estimates
- Assumptions of Fixed Effects Model
- Assumptions of Random Effects Model
- Difference between Random Effects and Fixed Effects “Within” Estimator
- Estimation of Random Effects Model with FGLS
- FGLS: Derivation of the error variances and weights for Random Effects using the Swamy-Arora method
- Introducing the Likelihood Function for the Random Effects Maximum Likelihood Estimator
- Application of Random Effects Model in R and Stata
- Estimating the individual-specific, time-specific and Two-way Random Effects in R and Stata
- Interpretation of Random Effects Estimates
- Obtaining the Weights or weights matrix used in Random Effects
- Pooled OLS vs Fixed Effects: F-test
- LM test for Random Effects
- Hausman Test for Fixed vs Random Effects
- Theory, Interpretation and Application in R and Stata
For complete details on the learning objectives, go to the link here: Fixed and Random Effects Models.
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